The Identification of Valid Performance Indicators in Non-Elite Volleyball | My Masters Dissertation

This is an abridge version of my Masters Dissertation.   Go to the end of the post to download a full copy of my Masters Dissertation.  

Purpose of the Study

The purpose of the study is to identify valid volleyball performance indicators of all technical skills and combined technical skills in non-elite volleyball using the original performance indicators using the methods of set outcome, close set outcome and final league points.

Introduction

in sport are a selection or combination of performance variables that describe some or all aspects of sports performance (Hughes & Bartlett, 2002). Performance indicators are when they are associated with a successful performance or outcome. They are important because it provides a deeper understanding to what is critical to success.  Success in volleyball has been defined as winning or losing a set (set outcome), winning or losing a close set (set is within two points, e.g. 25-23, 26-24) and final league ranking (first to eighth) or points.

Table 1 has a brief description of valid performance indicator studies in volleyball.  It shows overwhelmingly strong support for attack performance indicators as critical to success (Palao, et al., 2004). They showed mixed support for serve and block performance indicators, weak support for reception performance indicators and almost no support digs performance indicators as critical to success (Palao, et al., 2004). This does not reflect many coaches’ philosophy that a team cannot mount an effective attack if the first ball touch of reception or dig is poor (Nolen, 1997; Stone, 1999). A possible explanation for this is that data used for statistical analysis came from elite standard volleyball such as the Olympic Games. It could be argued teams can receive and dig effectively at this level so every team can mount an effective attack. However, at non-elite level, non-elite teams are not able to execute all six technical skills compared to the elite teams (Hughes & Daniel, 2003). This may mean that performance indicators at elite level may be different at non-elite level, which might include defence.

Method

The sample consisted of 180 sets taken from the Men’s National Volleyball League Super 8s 2012/13 season. In every set, teams were characterised according to set outcome (win or lose), close set outcome (win or lose where set score differences were two points) and final league points. The sets were video-recorded and analysed with the StatEasy program. Data was collected using an efficacy system where eight technical skills (attack, serve, block, setting, reception, dig, cover dig, free ball-overpass dig) and two combined skills (point scoring and ball control) were rated on a five-point scale.  Six performance indicators (efficiency ratio, efficiency percentage, skill rating four percentage, positive percentage, in-play percentage and efficacy) were produced for each technical and combined skill (see below).  Wilcoxon signed ranks test was conducted to identify differences between winning and losing performances within a set and a close set. Pearson’s correlation was conducted to identify correlations between the performance indicators and final league points.

Results

Wilcoxon signed ranks test results show 47 of the 60 original performance indicators covering all technical skills with the exception of free ball-overpass digs and cover digs were significantly different between winning and losing performances within a set (p < 0.01). Wilcoxon signed ranks test results show 11 of the 60 original performance indicators covering point scoring, ball control, attack and setting were significantly different between winning and losing performances within a set (p < 0.01).  Pearson’s correlation test results shows that 24 of the original 60 performance indicators covering point scoring, ball control, attack, setting and reception were significantly correlated to final league points (p < 0.01).  The two graphs shows a close correlation of points and attack efficiency percentages (figure 1) and efficiency ratio (figure 2) with league points.

Discussion

The set outcome results support the argument that valid performance indicators at elite level may be different at non-elite level, which might include defence.  Attack, serve, block, setting, reception and dig performance indicators are shown to be decisive in set outcome.  This means winning teams perform better compared to losing teams in nearly all technical skills.  However, which performance indicators are the best discriminators for success?

Figure 3 shows 11 overall performance indicators that significantly differentiated between winning and losing teams in a set and within a close set in addition to significantly correlates to league points. The top three involved point scoring performance indicators of points efficiency percentage, points efficiency ratio and points efficacy. The nature of these performance indicators involved both point scoring and point losing action variables. This shows that it is not enough to just maximise point scoring or minimise making errors but do both concurrently.  The next three best performance indicators is ball control efficiency ratio, ball control in-play percentage and ball control efficiency percentage. Ball control utilises all technical skills and it may show the importance of overall skill level on all technical skills. However, it may be the weight of the point scoring technical skills has made it valid. 

The two best individual technical skill performance indicator is the attack efficiency percentage (seventh overall) and attack efficacy (ninth overall). This is not a surprise as attack is the biggest contributor to points scoring and losing. In addition, every study has attack, in one performance indicator or another, as valid. Again, there is the emphasis of not only maximising kills but also reducing attack errors.   The next two best individual technical skill performance indicators is assist efficiency percentage (tenth overall) and assist percentage (eleventh overall). This is not a surprise as setting is a function of how well the attack is run. 

These results makes sense because for a team to be successful, they must win matches and to win matches, they must to win sets and to win a set, they must score 25 points before the opponent does.  This is why the ability to score points (and not lose points) is critical to success.  Attack makes sense too because because most points won and lost are done via the attack.  This reflects the previous research overwhelming showing support for attack as a valid performance indicator.

Dowload the PDF Copy of my Dissertation – Let me know (hai.binh.ly@gmail.com) if you ever reference me 🙂 Download

Valid Performance Indicator Studies Series

This is a part of the valid performance indicator studies series, including an overview of all five studies that I did.  Links are below.

6 | Overview of the five valid performance indicator studies 5 | City University London + Opponents (BUCS South Eastern Division 1), 2017-20, Set outcome 4 | IBB Polonia Men + Opponents (Men’s Super League), 2018-20, Set outcome 3 | Women’s Super 8s + League, 2015-20, League correlation (2017-18) + Set outcome 2 | Women’s Super League, 2019-20, League correlation + set outcome 1 | Men’s Super 8s, 2012-13, League correlation + set outcome (Masters Dissertation) 0 | Academic Literature Review, elite men’s volleyball (2004 – 2012)